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All Journal IPTEK Journal of Proceedings Series Jurnal Ilmu Komputer dan Agri-Informatika International Journal of Advances in Intelligent Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Building of Informatics, Technology and Science Journal of Robotics and Control (JRC) Jurnal Pusat Inovasi Masyarakat Cylinder : Jurnal Ilmiah Teknik Mesin
Karlisa Priandana
Computer Science Department Faculty Of Mathematics And Natural Sciences IPB University (Bogor Agricultural University)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Iis Rodiah; Medria Kusuma Dewi Hardhienata; Agus Buono; Karlisa Priandana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4201

Abstract

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.
Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States Hidayat; Agus Buono; Karlisa Priandana; Sri Wahjuni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4949

Abstract

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.
Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points Try Kusuma Wardana; Yandra Arkeman; Karlisa Priandana; Farohaji Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4959

Abstract

Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m.
Co-Authors Agus Buono Agus Buono Agus Buono Agus Buono Agus Buono Ahmad Zulfikar S Alex Coccia Ardinsyah Siregar Arsy Novita Syahada Arsy Novita Syahada Auzi Asfarian Bib Paruhum Silalahi Doli Aryo Dani Sinaga Doli Aryo Dani Sinaga Elisabeth Sri Hendrastuti Farohaji Kurniawan Hidayat Hidayat Hidayat Husin Alatas Iis Rodiah Irene Pricella Situmorang Irene Pricella Situmorang Iskandar Zulkarnaen Siregar Karina Eku Dwinanda Gunawan Laksono Trisnantoro Leo P. Ligthart M Alwan Farhandany Medria Kusuma Dewi Hardhienata Michael Julyus Christopher Manullang Muhammad Hazim Muhammad Rafi Muttaqin Nazala Hafidhullah Peter John Morley Puspita Kartika Sari Puspita Kartika Sari Sarah Arisanti Sri Wahjuni SUKARMAN Sumanto, Sumanto Try Kusuma Wardana WULANDARI Yandra Arkeman